Comparative analysis of traffic and congestion in software-defined networks

被引：0

作者：

Parihar A.S. ^{[1
]}

Sinha K. ^{[1
]}

Singh P. ^{[1
]}

Cherwoo S. ^{[1
]}

机构：

[1] Department of Computer Science and Engineering, Delhi Technological University, Delhi, New Delhi

来源：

Lecture Notes on Data Engineering and Communications Technologies | 2021年 / 66卷

关键词：

Congestion; Machine learning; Performance prediction; Software-defined networks; Traffic classification;

D O I：

10.1007/978-981-16-0965-7_69

中图分类号：

学科分类号：

摘要：

The different methods used for classifying traffic along with the prediction of congestion and performance in software-defined networks were discussed. Although congestion prediction has foreseen many challenges, the algorithms did not give very accurate results. But over a period of time, several methods have been discovered to identify and predict the performance and congestion in software-defined networks (SDN). In this article, various techniques of classification were compared and predicted through tables and graphs. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.

引用

页码：907 / 917

页数：10

共 16 条

[1]

Saied W., Souayeh N.B.Y.B., Saadaoui A., Bouhoula A., Deep and automated SDN data plane analysis, In: IEEE International Conference on Software, Telecommunications and Computer Networks, Croatia, (2019)

[2]

Smys S., Raj J.S., A stochastic mobıle data traffic model for vehicular ad hoc networks, J Ubiquitous Comput Commun Technol, 1, pp. 55-63, (2019)

[3]

Wu J., Peng Y., Song M., Cui M., Zhang L., Link congestion prediction using machine learning for software-defined-network data plane, IEEE International Conference on Computer Information and Telecommunication Systems, (2019)

[4]

McGregor A., Hall M., Lorier P., Brunskill J., Flow clustering using machine learning techniques, In: Proceedings of the 5Th International Passive and Active Network Measurement International Workshop, (2004)

[5]

Azzouni A., Boutaba R., Pujolle G., NeuRoute: Predictive dynamic routing for software-defined networks, International Conference on Network and Service Management (CNSM), (2017)

[6]

Leng B., Huang L., Qiao C., Xu H., A decision-tree-based on-line flow table compressing method in software defined networks, IEEE/ACM 24Th International Symposium Quality of Service (Iwqos), (2016)

[7]

Azzouni A., Pujolle G., NeuTM: A neural network-based framework for traffic matrix prediction in SDN, NOMS 2018-2018 IEEE/IFIP Network Operations and Management Symposium, (2018)

[8]

Fan Z., Liu R., Investigation of machine learning based network traffic classification, International Symposium on Wireless Communication Systems (ISWCS), (2017)

[9]

Amaral P., Dinis J., Pinto P., Bernardo L., Tavares J., Mamede H.S., Machine learning in software defined networks: Data collection and traffic classification, IEEE 24Th International Conference on Network Protocols (ICNP), (2016)

[10]

Xiao K., Mao S., Tugnait J.K., TCP-Drinc: Smart congestion control based on deep rein-forcement learning, IEEE Access, 7, pp. 11892-11904, (2019)

← 1 2 →